APOA5 -1131T>C and APOC3 -455T>C polymorphisms are...

©FUNPEC-RP www.funpecrp.com.brGenetics and Molecular Research 14 (4): 18218-18228 (2015)

APOA5 -1131T>C and APOC3 -455T>C polymorphisms are associated with an increased risk of coronary heart disease

Y. Sun, R.B. Zhou and D.M. Chen

Department of Emergency, The Military General Hospital of Beijing PLA, Beijing, China

Corresponding author: D.M. ChenE-mail: [email protected]

Genet. Mol. Res. 14 (4): 18218-18228 (2015)Received August 25, 2015Accepted October 31, 2015Published December 23, 2015DOI http://dx.doi.org/10.4238/2015.December.23.9

ABSTRACT. The aim of this study was to investigate correlations between apolipoprotein A-V (APOA5) -1131T>C and apolipoprotein C-III (APOC3) -455T>C polymorphisms and coronary heart disease (CHD). PubMed, Ovid, Cochrane Library, Embase, China National Knowledge Infrastructure, and Wanfang databases were searched using combinations of keywords relating to these polymorphisms and CHD. Studies retrieved from database searches were screened using our stringent inclusion and exclusion criteria, and Comprehensive Meta-Analysis Version 2.0 software was used for statistical analyses. In total, 115 studies were initially retrieved and after further selection, 11 were included in the meta-analysis. These 11 articles comprised 4840 patients with CHD in the case group and 4913 healthy participants in the control group. Meta-analysis revealed that APOA5 -1131T>C and APOC3 -455T>C polymorphisms increased CHD risk. In addition, subgroup analysis by ethnicity showed that while the -1131T>C polymorphism elevated the risk of CHD in the Caucasian population under both allelic and dominant models, this increased risk was observed only under a dominant model in the Asian

18219APOA5 and APOC3 polymorphisms and coronary heart disease


population. The results of our meta-analysis point to a strong link between both APOA5 -1131T>C and APOC3 -455T>C polymorphisms and an increased risk of CHD. Thus, these polymorphisms constitute important predictive indicators of CHD susceptibility.

Key words: Coronary heart disease; Apolipoprotein A-V; Apolipoprotein C-III; Polymorphism; Single nucleotide polymorphisms; Triglycerides

INTRODUCTION

Coronary heart disease (CHD) resulting from atherosclerosis, also known as coronary artery disease, is a serious condition in which plaque builds up inside the coronary arteries. Plaques harden and, over time, narrow the coronary arteries that supply blood and oxygen to the heart, ultimately resulting in myocardial infarction and potentially death. CHD is the leading cause of mortality worldwide, accounting for nearly 7 million deaths annually (Chen et al., 2014). For individuals older than 18 years, the prevalence of CHD ranges from 2.9 to 6.6%; the prevalence is varies by race and ethnicity, with American Indians and natives of Alaska having the highest prevalence and Asians having the lowest prevalence (Imes et al., 2013). Etiologically, both environmental and genetic factors contribute to the occurrence and development of CHD (Yu et al., 2011). The major risk factors are male gender, advancing age, elevated levels of plasma low-density lipoprotein cholesterol (LDL-C), low levels of high-density lipoprotein cholesterol (HDL-C), elevated blood pressure, obesity, and lifestyle factors such as smoking, a high-fat diet, and lack of exercise (Lettre et al., 2011). Elevated triglyceride (TG) and low HDL-C levels, irrespective of LDL-C levels, are typical of dyslipidemia, which puts individuals at high risk of CHD (Brautbar et al., 2011).

TGs are major components of plaque, and heightened TG levels correlate with an increased risk of CHD (Carey et al., 2010). Apolipoprotein A-V (APOA5) is a major determi-nant of plasma TG levels and is located next to sequences encoding apolipoproteins A-IV, C-III (APOC3), and A-I in a gene cluster on chromosome 11q23 (Soufi et al., 2012). APOA5 encodes a protein of 366 amino acids, which is secreted into plasma to control plasma TG metabolism, and whose expression is restricted to human liver tissue (Ouatou et al., 2014). Variants of APOA5 include -1131T>C and S19W (Yu et al., 2007). APOC3, another key regu-lator of plasma TG levels, is a glycoprotein consisting of 79 amino acids synthesized princi-pally in the liver and, to a lesser extent, the intestine. This protein is an inhibitor of lipoprotein lipase (LPL) and a component of very low-density lipoprotein (VLDL). An excess of APOC3 in plasma can result in elevated LDL levels and is positively associated with the progression of atherosclerosis and risk of CHD. Several polymorphisms have been found in the APOC3 gene, including C-482T, T-455C, Sst I, and C1100T. Moreover, accumulating evidence sug-gests that APOA5 -1131T>C and APOC3 -455T>C polymorphisms play a major role in the development of CHD because of their association with increased plasma TGs (Zhang et al., 2011; Zhou et al., 2013; Cui et al., 2014). On the other hand, many studies have reached con-tradictory conclusions regarding the role of APOC3 and APOA5 variants in CHD (Martinelli et al., 2007; Maasz et al., 2008; Prochaska et al., 2010; Yu et al., 2011). We therefore employed a meta-analysis approach to investigate the relationship between these two important poly-morphisms and CHD risk.

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MATERIAL AND METHODS

Search strategy

PubMed, Ovid, Cochrane Library, Embase, China National Knowledge Infrastructure, and Wanfang databases were searched for relevant studies published up to October 2014. Additionally, potentially useful studies were obtained by manual searches. The following subject terms and key words were used to maximize search specificity and sensitivity: coronary heart disease, apolipoproteinA5, apolipoproteinC3, and genetic polymorphisms.

Study selection

The following inclusion criteria were applied to screen published articles for the present analysis: (a) use of a case-control study design; (b) a research focus consisting of an assessment of correlations between CHD susceptibility and single nucleotide polymorphisms (SNPs) of APOA5 or APOC3; (c) subjects comprising patients with CHD in the case group and healthy individuals in the control group; (d) complete data relating to the first author, country, publication year, number, ethnicity, and sex of cases, genotyping method, and genotype distributions of -1131T>C and -455T>C; and (e) only the most recent studies or those with the largest sample sizes were considered when the extracted articles were published by the same authors, using the same case materials. The exclusion criteria were: (a) unclear diagnostic criteria regarding the included subjects; (b) only the latest complete study was considered when the extracted studies were published by the same authors ; and (c) lack of data integrity.

Data extraction and methodological quality evaluation

Two investigators collected data independently using a standardized data extraction form. We retrieved information related to the first author, publication year, country, language, disease, number, age, and ethnicity of cases, genotyping method, and genotype distributions of -1131T>C and -455T>C polymorphisms. Any difference of opinion during data extraction was resolved through discussion among multiple researchers.

Statistical methods

All statistical analyses were conducted using Comprehensive Meta-Analysis Version 2.0 software (Biostat Inc., Englewood, NJ, USA). Correlations between APOA5 and APOC3 SNPs and risk of CHD were estimated by the calculation of odds ratios (ORs) and 95% confidence intervals (95%CIs). A Z-test was employed to detect the significance of overall effect size (Chen et al., 2012), and forest plots were generated to display OR values and 95%CIs between case and control groups. Heterogeneity among studies was evaluated using Cochran’s Q-statistic (P < 0.05 was considered to signify evident heterogeneity) and the I2 test, which measures the percentage of total variation across studies, ranging from 0 to 100% (Peters et al., 2006; Jackson et al., 2012). A fixed-effects model was employed unless significant heterogeneity was detected (P < 0.05 or I2 > 50%), in which case a random-effects model was applied (Zintzaras et al., 2005). Univariate and multivariate meta-regression analyses were used to estimate sources of heterogeneity, and Monte



Carlo simulation was performed to correct and verify results (Ferrenberg et al., 1988; Huizenga et al., 2011, Jackson et al., 2012). One-way sensitivity analysis was carried out to evaluate whether the removal of a single study would influence the overall outcome. Publication bias, an assessment of the reliability of the results, was evaluated by funnel plot, Egger test (Egger et al., 1997; Sterne et al., 2001), and classic fail-safe N (Wikstrom et al., 2009). All tests were two-sided, with P < 0.05 indicating a significant difference.

RESULTS

Study selection and baseline characteristics

Our search strategy retrieved 115 citations. In total, 64 studies remained after initially excluding 5 duplicates, 14 animal studies, 26 studies unrelated to the research topic, and 6 letters, reviews, or meta-analyses. Next, we excluded 14 cohort studies, 22 studies not relevant to APOA5 and APOC3, 12 studies unrelated to CHD, and 5 studies containing insufficient information. Finally, 11 case-control studies (Bassi et al., 2003; Bi et al., 2004; Olivieri et al., 2005; Hsu et al., 2006; Zhu et al., 2007; Yu et al., 2007; Martinelli et al., 2007; Li et al., 2007; Jang et al., 2009; De Caterina et al., 2011; Bhaskar et al., 2011) published between 2003 and 2011 (containing in total 4840 CHD patients in the case group and 4913 healthy controls in the control group) were eventually selected for this meta-analysis. Of these, 7 studies involved Asian populations, with 5 conducted in China, 1 in Korea, and 1 in India. The other 4 studies comprised Caucasian populations in Italy. Sample sizes of included studies varied between 138 and 1864. The baseline characteristics of all included case-control studies are shown in Table 1.

First author Country Ethnicity Genotyping Age (years) Gene SNP Case Control method group group

Cases Controls

De Caterina (2011) Italy Caucasian TaqMan array - - APOA5 -1131T>C 1864 1864Bhaskar (2011) India Asian PCR-RFLP - - APOA5 -1131T>C 250 120Jang (2009) Korea Caucasian PCR-RFLP 55.2 ± 0.32 55.2 ± 0.32 APOA5 -1131T>C 741 741Zhu (2007) China Asian PCR-RFLP 57.2 ± 12.1 58.3 ± 11.2 APOA5 -1131T>C 119 210Yu (2007) China Asian PCR-RFLP 52.1 ± 7.2 51.5 ± 8.7 APOA5 -1131T>C 140 156Li (2007) China Asian PCR-RFLP 62.80 ± 9.51 61.32 ± 9.43 APOA5 -1131T>C 186 268Martinelli (2007) Italy Caucasian PCR-RFLP 60.7 ± 9.3 58.7 ± 12.7 APOA5 -1131T>C/-455T>C 669 224Hsu (2006) China Asian PCR-RFLP 61.6 ± 8.9 55.1 ± 9.8 APOA5 -1131T>C 211 677Olivieri (2005) Italy Caucasian PCR-RFLP 60.5 ± 9.7 58.3 ± 12.8 APOC3 -−455T > C 210 206Bi (2004) China Asian PCR-RFLP 60.22 ± 13.30 58.87 ± 14.27 APOA5 -1131T>C 312 317Bassi (2003) Italy Caucasian PCR-RFLP 30.3 ± 9.4 57.9 ± 12.4 APOC3 -−455T > C 138 130

PCR-RFLP = polymerase chain reaction-restriction fragment length polymorphism; APOA5 = apolipoprotein A-V; APOC3 = apolipoprotein C-III; SNP = single nucleotide polymorphism.

Table 1. Baseline characteristics of studies included in this meta-analysis.

Association between APOA5 -1131T>C and CHD risk

Nine of the included studies investigated the correlation between the APOA5 -1131T>C SNP and risk of CHD. As heterogeneity was detected across allelic and dominant models (P < 0.05), a random-effects model was employed. As presented in Figure 1 and Table 2, the results of our meta-analysis revealed that the -1131T>C polymorphism was associated with an increased risk of CHD (allelic model: OR = 0.64, 95%CI = 0.44-0.94, P = 0.02; dominant model: OR =

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1.55, 95%CI = 1.17-2.06, P = 0.003). Subgroup analysis by ethnicity suggested that in Asian populations, this SNP might increase the risk of CHD under the dominant model (OR = 1.23, 95%CI = 1.10-1.36, P < 0.001), while no significant association was observed under the allelic model (OR = 0.58, 95%CI = 0.32-1.06, P = 0.077). On the other hand, in Caucasian populations, -1131T>C was associated with an increased risk of CHD using both models (allelic model: OR = 0.76, 95%CI = 0.68-0.84, P < 0.001; dominant model: OR = 1.39, 95%CI = 1.22-1.57, P < 0.001; Figure 2). In addition, we discovered a possible association between the -1131T>C allele and elevated plasma TG levels, while no significant relationship was observed between the APOA5 polymorphism and plasma levels of LDL-C and HDL-C. Univariate and meta-regression analysis revealed that publication year, country, ethnicity, and detection methods were not the main sources of heterogeneity or the key factors influencing overall effect size (P > 0.05). Moreover, multivariate meta-regression analysis suggested that these factors were not sources of heterogeneity. Results of meta-regression analyses are displayed in Figure 3 and Table 3.

Figure 1. Forest plots of the relationship between apolipoprotein C-III-455T>C and apolipoprotein A-V-1131T>C polymorphisms and coronary heart disease. CI = confidence interval; Std = standard deviation.

SNP -1131T>C -455T>C

OR 95%CI P OR 95%CI P

Allelic model 0.64 0.44-0.94 0.02 1.56 1.25-1.94 <0.001Dominant model 1.55 1.17-2.06 0.003 0.57 0.44-0.75 <0.001Homozygous model 1.82 1.26-2.62 0.001 0.61 0.39-0.97 0.037Heterozygous model 0.84 0.69-1.02 0.078 1.00 0.91-1.09 0.913Recessive model 1.80 1.08-3.00 0.025 0.71 0.44-1.14 0.157

SNP = single nucleotide polymorphism; OR = odds ratio; CI = confidence interval.

Table 2. Meta-analysis results comparing genotype and allele frequencies between the case and control groups.

Association between APOC3 -455T>C and CHD risk

Three of the included studies investigated the correlation between the APOC3 -455T>C SNP and risk of CHD. No heterogeneity was detected across allelic and dominant models, and thus a fixed-effects model was employed (P > 0.05). As presented in Figure 1, our meta-analysis showed that the -455T>C polymorphism increased the risk of CHD (allelic model: OR = 1.56, 95%CI = 1.25-1.94, P < 0.001; dominant model: OR = 0.57, 95%CI = 0.44-0.75, P < 0.001). In addition, we observed no significant correlation between APOC3 -455T>C and plasma TG, LDL-C, or HDL-C levels.



Figure 2. Subgroup analysis forest plots of the relationship between the apolipoprotein A-V-1131T>C polymorphism and coronary heart disease.

Figure 3. Meta-regression analyses assessing possible sources of heterogeneity and the impact of various factors on the observed relationship between the apolipoprotein A-V-1131T>C polymorphism and coronary heart disease. Adj = adjusted; PCR-RFLP = polymerase chain reaction-restriction fragment length polymorphism. A. Regression of year on log odds ratio. B. Regression of country on log odds ratio. C. Regression of ethnicity on log odds ratio. D. Regression of method on log odds ratio.

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Sensitivity analysis and publication bias

Sensitivity analysis suggested that no single study affected the overall OR values of correlations between APOA5 -1131T>C and APOC3 -455T>C SNPs and the risk of CHD (Figure 4). Funnel plots for the -1131T>C polymorphism under allelic and dominant models and the -455T>C SNP under a dominant model were symmetrical, indicating no significant publication bias (P > 0.05; Figure 5). Classic fail-safe N and Egger tests further verified an absence of such bias (P > 0.05). However, asymmetrical funnel plots were observed regarding the -455T>C polymorphism under an allelic model, suggesting significant publication bias. Classic fail-safe N and Egger tests confirmed this.

Heterogeneity factors Coefficient SEM t P (adjusted) 95%CI

LL UL

Year -0.03 0.045 -0.67 0.842 -0.156 0.095Country -0.002 0.108 -0.02 1.00 -0.303 0.298Ethnicity -0.031 0.219 -0.14 0.999 -0.639 0.577Method -0.267 0.279 -0.95 0.743 -1.042 0.509

SEM = standard error of mean; CI = confidence interval; LL = lower limit; UL = upper limit.

Table 3. Meta-regression analysis of potential sources of heterogeneity.

Figure 4. Sensitivity analysis testing the observed relationship between apolipoprotein C-III-455T>C and apolipoprotein A-V-1131T>C polymorphisms and coronary heart disease. CI = confidence interval.

DISCUSSION

HD, resulting from atherosclerosis, is a severe condition involving the build-up of plaque inside coronary arteries (Imes et al., 2013). Elevated TGs and low levels of HDL-C, irrespective of LDL-C levels, result in high CHD risk (Brautbar et al., 2011). APOC3 and APOA5 regulate TG levels and are associated with lipid metabolism, insulin resistance, and acute coronary syndrome (Ding et al., 2012; Garelnabi et al., 2013). To investigate the correlation between APOA5 -1131T>C



and APOC3 -455T>C SNPs and CHD, we conducted the present meta-analysis and found that both polymorphisms, under both allelic and dominant models, were positively associated with an increased risk of CHD. APOA5 encodes APOA5, a protein consisting of 366 amino acids that is exclusively expressed in human liver tissue and enhances LPL activity (Zhou et al., 2013). The loss of LPL activity interferes with the ability of APOA5 to interact with lipids and lipoproteins, including TGs, VLDLs, and HDLs (Dorfmeister et al., 2008; Johansen et al., 2011). Furthermore, the elevation of plasma TGs is a known CHD risk factor, and APOA5 status constitutes a major risk factor owing to its activation of TG hydrolysis in the blood (Carey et al., 2010). Genetic variations in the APOA5 gene have been shown to be associated with CHD. A correlation between the APOA5 gene region and CHD was established in a large meta-analysis in which the -1131T>C APOA5 promoter SNP was associated with both elevated TG levels and increased CHD events (Triglyceride Coronary Disease Genetics et al., 2010). However, it is unclear whether there is a causal relationship between this polymorphism and CHD. One potential mechanism involves a reduction in APOA5 expression caused by variation in the APOA5 promoter, leading to decreased TG hydrolysis in the blood, and thus elevated TG levels, which correlate with CHD susceptibility.

APOC3, a 79-amino acid glycoprotein synthesized mainly in the liver, and to a lesser extent in the intestine, functions as a key regulator of serum TG levels. Further, it acts as a constituent of triglyceride-rich lipoprotein particles, inhibiting their LPL-induced hydrolysis, and interferes with receptor-mediated lipoprotein uptake in liver (Yu et al., 2011). However, the -455T>C variant reduces the binding of transcription factors to its promoter elements, resulting in the dyslipidemia and abnormal glucose homeostasis associated with CHD risk (Miller et al., 2007). Interestingly, Yu et al. (2011) failed to find an association between the APOC3 -455T>C SNP and increased CHD risk in a Han Chinese population, although promoter variants did have a significant impact on plasma TG and APOC3 levels. Therefore, the results of epidemiological studies need to be supported by extensive experimental evidence, in order to thoroughly understand the underlying aberrations caused by polymorphisms and the mechanisms by which they lead to CHD.

Figure 5. Assessments of publication bias relating to the observed relationship between apolipoprotein C-III-455T>C and apolipoprotein A-V-1131T>C polymorphisms and coronary heart disease. d.f. = degrees of freedom; SEM = standard error of the mean. (Red and blue represent the two-tailed test of our study; Circles stand for the number of enrolled study; Rhombuses represent effect value).

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Subgroup analysis based on ethnicity suggested that in Asian populations, APOA5 -1131T>C might increase the risk of CHD under the dominant model. In Caucasian populations however, this SNP was associated with an increased risk of CHD under both allelic and dominant models. This difference may be attributed to different regional influences, ethnicities, and lifestyles.

Some limitations of this study should be noted. First, the number of CHD patients and healthy controls was relatively small in some of the included trials, which may reduce confidence in findings of the present meta-analysis. Second, this meta-analysis included data from Asian and Caucasian populations; however, no studies involving other ethnicities and mixed-race groups were included. Thus, our study may not be representative of all ethnicities. In addition, the present work was restricted to studies published in English or Chinese only, excluding investigations published in other languages, which may have led to selection bias.

In summary, our study revealed that APOA5 -1131T>C and APOC3 -455T>C polymorphisms, under both allelic and dominant models, were strongly associated with an increased risk of CHD, suggesting that they could be important indicators in predicting susceptibility to this disease.

Conflicts of interest

The authors declare no conflict of interest.

ACKNOWLEDGMENTS

We would like to thank our instructors for their valuable advice, for which we are grateful. We also appreciate the efforts of the reviewers of this paper for their valued comments.

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